Democracy and Accountability
Basic idea thus far:
Democracy and Accountability
Funders have had the same idea!
- A lot of money has been spent on “information interventions”
- Tell voters of politicians’ good/bad behavior
- And yet, we do not really know if they work
- It has been difficult to accumulate information
Barriers hindering accumulation
What is accumulation?
- Building knowledge across studies
- Limited replication
- Heterogeneity of design and measurement
- Publication bias
Publication bias
Do you think it is easier to publish a study that finds an effect or one that does not find any effect?
What are the consequences for what we, as a discipline, think we know?
Facilitating accumulation
What solutions are discussed by the authors?
- Pre-registration
- Harmonizing theory, measurement, and estimation
- Publication of null results
Facilitating accumulation
Models of political accountability
Accountability requires that voters:
- Observe performance
- Attribution (who’s fault?)
- Benchmarking (is this good or bad relative to what I thought?)
- Update their beliefs (learn from what they see)
- Have credible alternatives
Models of political accountability
What does the literature say?
- Theory is mixed
- Partisan and sectarian attachments are strong
- Voters may be reluctant to update their beliefs
- Experimental results are mixed
- Demobilization
- Limited recall
- Ephemeral effects
Research Design
Intervention
- Information on political performance
- Legislative behavior
- Spending irregularities
- Budget allocation
- Candidate experience
Research Design
Accountability requires that voters:
- Observe credible signal of performance
- Update their beliefs
- Good news: \(Q > P \mid 1(P = Q, Q > med(Q) )\)
- Have credible alternatives
Ecological Validity
- How is information disseminated?
- What is the real world activity being tested?
Research Design
Core hypotheses:
- Good news increases voter support for incumbents
- Bad news decreases voter support for incumbents
- Effect of information will increase with gap between Q and P
- Strongest for nonpartisan and non-coethnic voters
Findings
Findings
Why the null results?
- Source credibility?
- Lack of retention?
- Lack of updating on politician performance?
- Lack of updating about politician quality?
- Intervention is too weak?
Statistical Power
Can we update on “null results”?
Statistical Power
Recall the interpretation of p-values:
- The probability of observing a test statistic at least as extreme as the one you observed if the true parameter value is zero
- Or, the probability of rejecting the null hypothesis when the null was true
- This is called a “Type I” error: a false positive
- We also have “Type II” errors: a false negative
Statistical Power
Null results were not “foregone conclusion”?
- 80% power
- Change the vote of 5/100 voters
- Change turnout of 4/100 voters
Findings
Findings
Findings
Policy Implications
- Much of the work funded by donors is probably not having an impact on accountability
- Targeting of information provision needs to be rethought
- Public dissemination and coordination